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Dynamic Positive Reinforcement For Long-Term Fairness
Bhagyashree Puranik · Upamanyu Madhow · Ramtin Pedarsani

As AI-based decision-making becomes increasingly impactful on human society, the study of the influence of fairness-aware policies on the population becomes important. In this work, we propose a framework for sequential decision-making aimed at dynamically influencing long-term societal fairness, illustrated via the problem of selecting applicants from a pool consisting of two groups, one of which is under-represented. We consider a dynamic model for the composition of the applicant pool, where the admission of more applicants from a particular group positively reinforces more such candidates to participate in the selection process. Under such a model, we show the efficacy of the proposed Fair-Greedy selection policy which systematically trades greedy score maximization against fairness objectives. In addition to experimenting on synthetic data, we adapt static real-world datasets on law school candidates and credit lending to simulate the dynamics of the composition of the applicant pool.

Author Information

Bhagyashree Puranik (University of California Santa Barbara)
Upamanyu Madhow (University of California, Santa Barbara)
Ramtin Pedarsani (University of California, Santa Barbara)

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